{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f29d01f-7eb4-469e-b1cc-da82f8386e3f",
   "metadata": {},
   "source": [
    "# 实验5 分类模型的评估方法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ddffb15-8875-48a7-b003-a579d03eeae3",
   "metadata": {},
   "source": [
    "## 1.使用scikit-learn库生成二分类和多分类问题的虚拟数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35ace902-5122-4556-95a4-46425b3e7a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "label_size = 18 # Label size\n",
    "ticklabel_size = 14 # Tick label size\n",
    "\n",
    "# Generate moon-shaped data\n",
    "X, Y = make_moons(n_samples=1000, noise=0.25, random_state=42)\n",
    "\n",
    "# Split X and Y into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Plot the data\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], color='red', label='Positive - Train')\n",
    "ax.scatter(X_test[y_test == 1, 0], X_test[y_test == 1, 1], color='orange', label='Positive - Test')\n",
    "ax.scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], color='blue', label='Negative - Train')\n",
    "ax.scatter(X_test[y_test == 0, 0], X_test[y_test == 0, 1], color='green', label='Negative - Test')\n",
    "ax.set_xlabel('Feature 1', fontsize=label_size)\n",
    "ax.set_ylabel('Feature 2', fontsize=label_size)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Set legend fontsize\n",
    "plt.legend(prop={'size': 14})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f820c7e-b098-4c4b-bf0d-d387af13d681",
   "metadata": {},
   "source": [
    "## 2.训练逻辑回归和支持向量机做二分类，决策树和随机森林做多分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd70c1ac-a843-4c70-930b-06bd276ac888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model trained successfully...\n",
      "Support vector machine trained successfully...\n",
      "Decision tree trained successfully...\n",
      "Random forests trained successfully...\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Definition\n",
    "mdl_lr = LogisticRegression() # Logistic Regression\n",
    "mdl_svm = SVC() # Support Vector Machine\n",
    "mdl_dt = DecisionTreeClassifier() # Decision Tree\n",
    "mdl_rf = RandomForestClassifier(n_estimators=100) # Random Forests\n",
    "\n",
    "# Model Training\n",
    "mdl_lr.fit(X_train, y_train) # Logistic Regression\n",
    "print('Logistic regression model trained successfully...')\n",
    "\n",
    "mdl_svm.fit(X_train, y_train) # Support Vector machine\n",
    "print('Support vector machine trained successfully...')\n",
    "\n",
    "mdl_dt.fit(X_train, y_train) # Decision Tree\n",
    "print('Decision tree trained successfully...')\n",
    "\n",
    "mdl_rf.fit(X_train, y_train) # Random Forest\n",
    "print('Random forests trained successfully...')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84946062-1ad0-421a-8bfd-3b40b8cb4b78",
   "metadata": {},
   "source": [
    "## 3.绘制二分类和多分类模型的混淆矩阵并计算精度"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e5677d-2b1e-4292-a150-e1dd39ba2650",
   "metadata": {},
   "source": [
    "### 混淆矩阵 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd6e8d38-5b25-4f26-aab6-d8380cbb9487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns # To display confusion matrix\n",
    "    \n",
    "def confusion_matrix(y_pred, y):\n",
    "    '''\n",
    "    y_pred - predict classes\n",
    "    y - actual classes\n",
    "    '''\n",
    "    # Get the number of classes\n",
    "    n_classes = len(np.unique(y))\n",
    "    \n",
    "    # Initialize the confusion matrix with zeros\n",
    "    cm = np.zeros((n_classes, n_classes))\n",
    "    \n",
    "    # Fill the confusion matrix\n",
    "    for pred, actual in zip(y_pred, y):\n",
    "        cm[pred][actual] += 1\n",
    "    \n",
    "    return cm\n",
    "\n",
    "def cm_disp(cm, title='Confusion Matrix', save_fig=False):\n",
    "    ''' \n",
    "    Display confusion matrix\n",
    "    '''\n",
    "    global label_size, ticklabel_size\n",
    "    \n",
    "    # Create a figure and axis\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    \n",
    "    # Use seaborn to create a heatmap without color bar\n",
    "    sns.heatmap(cm, annot=True, fmt='.0f', cmap='Blues', ax=ax, annot_kws={'size': ticklabel_size}, cbar=False)\n",
    "    \n",
    "    # Set ticklabels\n",
    "    ax.set_xticklabels(['Positive', 'Negative'])\n",
    "    ax.set_yticklabels(['Positive', 'Negative'], rotation=90)\n",
    "    \n",
    "    # Set labels and title with custom font sizes\n",
    "    ax.set_xlabel('Predicted labels', fontsize=label_size)\n",
    "    ax.set_ylabel('True labels', fontsize=label_size)\n",
    "    ax.set_title(title, fontsize=label_size)\n",
    "    \n",
    "    # Set tick label font size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "    \n",
    "    if save_fig:\n",
    "        plt.savefig(f'{title.replace(\" \", \"_\")}_confusion_matrix.png', dpi=300, bbox_inches='tight')\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "    \n",
    "# Get confusion matrix of all models\n",
    "# Predictions for each model\n",
    "y_pred_lr = mdl_lr.predict(X_test)\n",
    "y_pred_svm = mdl_svm.predict(X_test)\n",
    "y_pred_dt = mdl_dt.predict(X_test)\n",
    "y_pred_rf = mdl_rf.predict(X_test)\n",
    "\n",
    "# Confusion matrices for each model\n",
    "cm_lr = confusion_matrix(y_pred_lr, y_test)\n",
    "cm_svm = confusion_matrix(y_pred_svm, y_test)\n",
    "cm_dt = confusion_matrix(y_pred_dt, y_test)\n",
    "cm_rf = confusion_matrix(y_pred_rf, y_test)\n",
    "\n",
    "# Display the confusion matrices\n",
    "save_flag = False\n",
    "cm_disp(cm_lr, title='Logistic Regression', save_fig=save_flag)\n",
    "cm_disp(cm_svm, title='Support Vector Machine', save_fig=save_flag)\n",
    "cm_disp(cm_dt, title='Decision Tree', save_fig=save_flag)\n",
    "cm_disp(cm_rf, title='Random Forest', save_fig=save_flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05943b51-3e0a-4604-a722-5c50e4173c87",
   "metadata": {},
   "source": [
    "###  计算各模型的准确率 Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "710b9235-e7be-4bf6-848f-92305690221d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.8667\n",
      "Support Vector Machine Accuracy: 0.9600\n",
      "Decision Tree Accuracy: 0.9200\n",
      "Random Forest Accuracy: 0.9500\n"
     ]
    }
   ],
   "source": [
    "def getAccuracyFromCM(cm):\n",
    "    # Calculate total number of samples\n",
    "    total = np.sum(cm)\n",
    "    \n",
    "    # Calculate number of correct predictions (sum of diagonal elements)\n",
    "    correct = np.trace(cm)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = correct / total\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "# Accuracy of each model\n",
    "acc_lr = getAccuracyFromCM(cm_lr)\n",
    "acc_svm = getAccuracyFromCM(cm_svm)\n",
    "acc_dt = getAccuracyFromCM(cm_dt)\n",
    "acc_rf = getAccuracyFromCM(cm_rf)\n",
    "\n",
    "# Print accuracies\n",
    "print(f\"Logistic Regression Accuracy: {acc_lr:.4f}\")\n",
    "print(f\"Support Vector Machine Accuracy: {acc_svm:.4f}\")\n",
    "print(f\"Decision Tree Accuracy: {acc_dt:.4f}\")\n",
    "print(f\"Random Forest Accuracy: {acc_rf:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf58e01-6bd4-41d7-8130-fb71bc45ca03",
   "metadata": {},
   "source": [
    "## 4.使用scikit-learn库的precision_recall_curve绘制P-R曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eac1a0c-b133-472e-8ee4-b821e32106a7",
   "metadata": {},
   "source": [
    "### 使用逻辑回归和SVM进行分类，输出概率\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a79b54fb-99f2-4dce-94c4-9bb64613ba7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model trained successfully...\n",
      "Support vector machine trained successfully...\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Model Definition\n",
    "mdl_lr = LogisticRegression() # Logistic Regression\n",
    "mdl_svm = SVC(probability=True) # Support Vector Machine, set probability to draw P-R Curve\n",
    "\n",
    "# Model Training\n",
    "mdl_lr.fit(X_train, y_train) # Logistic Regression\n",
    "print('Logistic regression model trained successfully...')\n",
    "\n",
    "mdl_svm.fit(X_train, y_train) # Support Vector machine\n",
    "print('Support vector machine trained successfully...')\n",
    "\n",
    "# Get confusion matrix of all models\n",
    "# Predict probabilities for each model\n",
    "y_proba_lr = mdl_lr.predict_proba(X_test)[:, 1] # Using probability of positive class\n",
    "y_proba_svm = mdl_svm.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1306b372-5f40-4210-9a0d-9771a8df95ed",
   "metadata": {},
   "source": [
    "### 调整阈值，改变精确率和召回率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e52eeaf-4809-44ba-9b8a-fc02561cb8e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix(y_pred, y):\n",
    "    '''\n",
    "    y_pred - predict classes\n",
    "    y - actual classes\n",
    "    '''\n",
    "    # Get the number of classes\n",
    "    n_classes = len(np.unique(y))\n",
    "    \n",
    "    # Initialize the confusion matrix with zeros\n",
    "    cm = np.zeros((n_classes, n_classes))\n",
    "    \n",
    "    # Fill the confusion matrix\n",
    "    for pred, actual in zip(y_pred, y):\n",
    "        cm[pred][actual] += 1\n",
    "    \n",
    "    return cm\n",
    "\n",
    "def get_prediction_from_proba(y_proba, threshold=0.5):\n",
    "    '''\n",
    "    y_proba - predict probabilities\n",
    "    threshold - threshold of probability\n",
    "    \n",
    "    y_pred - predict classes, y_proba > threshold is positive, otherwise negative\n",
    "    '''\n",
    "    y_pred = np.where(y_proba > threshold, 1, 0)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44fa0428-eefe-42f8-b143-2748fb613558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When threshold = 0.0 - Precision: 0.4800, Recall: 1.0000, F1 Score: 0.6486\n",
      "When threshold = 0.1 - Precision: 0.6575, Recall: 1.0000, F1 Score: 0.7934\n",
      "When threshold = 0.2 - Precision: 0.7473, Recall: 0.9653, F1 Score: 0.8424\n",
      "When threshold = 0.3 - Precision: 0.7824, Recall: 0.9236, F1 Score: 0.8471\n",
      "When threshold = 0.4 - Precision: 0.8062, Recall: 0.8958, F1 Score: 0.8487\n",
      "When threshold = 0.5 - Precision: 0.8514, Recall: 0.8750, F1 Score: 0.8630\n",
      "When threshold = 0.6 - Precision: 0.9008, Recall: 0.8194, F1 Score: 0.8582\n",
      "When threshold = 0.7 - Precision: 0.9106, Recall: 0.7778, F1 Score: 0.8390\n",
      "When threshold = 0.8 - Precision: 0.9808, Recall: 0.7083, F1 Score: 0.8226\n",
      "When threshold = 0.9 - Precision: 1.0000, Recall: 0.5278, F1 Score: 0.6909\n",
      "When threshold = 1.0 - Precision: 0.0000, Recall: 0.0000, F1 Score: 0.0000\n",
      "[[156. 144.]\n",
      " [  0.   0.]]\n"
     ]
    }
   ],
   "source": [
    "def get_precision_recall_f1(y_pred, y):\n",
    "    '''\n",
    "    Compute precision, recall, and F1 score of binary classification\n",
    "    y_pred - predict classes\n",
    "    y - actual classes\n",
    "    '''\n",
    "    epsilon = 1e-10\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_pred, y)\n",
    "    \n",
    "    # Compute precision, recall, and F1 score\n",
    "    precision = cm[1, 1] / (cm[1, 1] + cm[1,0] + epsilon)\n",
    "    recall = cm[1, 1] / (cm[1, 1] + cm[0, 1] + epsilon)\n",
    "    f1 = 2 * (precision * recall) / (precision + recall + epsilon)\n",
    "    \n",
    "    return precision, recall, f1\n",
    "\n",
    "# Get predictions from probabilities\n",
    "for threshold in np.linspace(0, 1, 11):\n",
    "    y_pred_lr = get_prediction_from_proba(y_proba_lr, threshold)\n",
    "    precision_lr, recall_lr, f1_lr = get_precision_recall_f1(y_pred_lr, y_test)\n",
    "    print(f\"When threshold = {threshold:0.1f} - Precision: {precision_lr:.4f}, Recall: {recall_lr:.4f}, F1 Score: {f1_lr:.4f}\")\n",
    "\n",
    "print(confusion_matrix(y_pred_lr, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1490e79a-59fd-433c-92da-e5caf6e321cf",
   "metadata": {},
   "source": [
    "### 调整Threshold，绘制P-R曲线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "910f268c-a9d5-43b2-8213-306afa0d0120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# Calculate precision and recall for various thresholds\n",
    "precision_lr, recall_lr, thresholds_lr = precision_recall_curve(y_test, y_proba_lr)\n",
    "precision_svm, recall_svm, thresholds_svm = precision_recall_curve(y_test, y_proba_svm)\n",
    "\n",
    "# Create the P-R curve of Logistic Regression and Support Vector Machine\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(recall_lr, precision_lr, marker='.', color='tab:blue', label='Model 2')\n",
    "ax.plot(recall_svm, precision_svm, marker='.', color='tab:orange', label='Model 3')\n",
    "ax.set_xlabel('Recall', fontsize=label_size)\n",
    "ax.set_ylabel('Precision', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "plt.legend(fontsize=ticklabel_size)\n",
    "\n",
    "# Add some threshold annotations of Logistic Regression\n",
    "for i in [0, int(len(thresholds_lr)/2), len(thresholds_lr)-1]:\n",
    "    plt.annotate(f'Threshold: {thresholds_lr[i]:.2f}', \n",
    "                    xy=(recall_lr[i]*0.8, thresholds_lr[i]), \n",
    "                    xytext=(5, 5), \n",
    "                    textcoords='offset points',\n",
    "                    fontsize=ticklabel_size)\n",
    "\n",
    "plt.savefig('P-R_Curve.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43d39cc-0cb2-45e3-b965-f4c87ef85ac4",
   "metadata": {},
   "source": [
    "## 5.绘制F1-Recall曲线，确定最优判定阈值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "87702aa0-1836-4597-81db-c12b61ec0701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate F1 scores for SVM model\n",
    "f1_svm = 2 * (precision_svm * recall_svm) / (precision_svm + recall_svm)\n",
    "\n",
    "# Drawing a curve with Recall as x-axis and F1-score as y-axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(recall_svm, f1_svm, marker='.', color='tab:orange', label='Model 3')\n",
    "ax.set_xlabel('Recall', fontsize=label_size)\n",
    "ax.set_ylabel('F1 Score', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Add some threshold annotations of SVM where F1-score is maximum\n",
    "max_f1_index = np.argmax(f1_svm)\n",
    "plt.annotate(f'Threshold: {thresholds_svm[max_f1_index]:.2f}', \n",
    "                    xy=(recall_svm[max_f1_index]*0.8, f1_svm[max_f1_index]), \n",
    "                    xytext=(5, 5), \n",
    "                    textcoords='offset points',\n",
    "                    fontsize=ticklabel_size)\n",
    "\n",
    "# Adding a dashed line to indicate the maximum F1 score\n",
    "plt.axvline(x=recall_svm[max_f1_index], color='tab:orange', linestyle='--')\n",
    "plt.axhline(y=f1_svm[max_f1_index], color='tab:orange', linestyle='--')\n",
    "\n",
    "# Adding text annotations for maximum F1 score\n",
    "plt.text(recall_svm[max_f1_index]-0.8, f1_svm[max_f1_index]-0.05, f'Max F1 Score: {f1_svm[max_f1_index]:.4f}', \n",
    "         horizontalalignment='left', verticalalignment='bottom', fontsize=ticklabel_size, color='tab:orange')\n",
    "\n",
    "# Adding text annotations below x-axis of Recall where F1-score is maximum\n",
    "plt.text(recall_svm[max_f1_index]-0.3, -0.05, f'Recall: {recall_svm[max_f1_index]:.4f}', \n",
    "         horizontalalignment='left', verticalalignment='bottom', fontsize=ticklabel_size, color='tab:orange')\n",
    "\n",
    "plt.savefig('F1_Curve.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05162b07-f555-451e-be6a-cf5c28e72363",
   "metadata": {},
   "source": [
    "## 6.计算敏感性和特异性，绘制ROC曲线并计算AUC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bb76f7-825e-4e59-bc8c-492b6034b295",
   "metadata": {},
   "source": [
    "### SVM进行分类，输出概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3fa027c-e393-47bb-92a8-cda25bd685d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support vector machine trained successfully...\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Model Definition\n",
    "mdl_svm = SVC(probability=True)\n",
    "\n",
    "mdl_svm.fit(X_train, y_train) # Support Vector machine\n",
    "print('Support vector machine trained successfully...')\n",
    "\n",
    "# Predict probabilities\n",
    "y_proba_svm = mdl_svm.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd1703e-2146-44ab-936c-3e4a9ce01c14",
   "metadata": {},
   "source": [
    "### 生成混淆矩阵，计算敏感性和特异性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1e2d1926-a12c-4ce3-a8e4-514e46493919",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When threshold = 0.00 - Sensitivity: 1.0000, Specificity: 0.0000\n",
      "When threshold = 0.01 - Sensitivity: 1.0000, Specificity: 0.3846\n",
      "When threshold = 0.02 - Sensitivity: 1.0000, Specificity: 0.6346\n",
      "When threshold = 0.03 - Sensitivity: 1.0000, Specificity: 0.7115\n",
      "When threshold = 0.04 - Sensitivity: 1.0000, Specificity: 0.7436\n",
      "When threshold = 0.05 - Sensitivity: 1.0000, Specificity: 0.7756\n",
      "When threshold = 0.06 - Sensitivity: 0.9931, Specificity: 0.8077\n",
      "When threshold = 0.07 - Sensitivity: 0.9931, Specificity: 0.8141\n",
      "When threshold = 0.08 - Sensitivity: 0.9931, Specificity: 0.8333\n",
      "When threshold = 0.09 - Sensitivity: 0.9931, Specificity: 0.8462\n",
      "When threshold = 0.10 - Sensitivity: 0.9931, Specificity: 0.8526\n",
      "When threshold = 0.11 - Sensitivity: 0.9861, Specificity: 0.8654\n",
      "When threshold = 0.12 - Sensitivity: 0.9861, Specificity: 0.8782\n",
      "When threshold = 0.13 - Sensitivity: 0.9861, Specificity: 0.8846\n",
      "When threshold = 0.14 - Sensitivity: 0.9861, Specificity: 0.8846\n",
      "When threshold = 0.15 - Sensitivity: 0.9861, Specificity: 0.8910\n",
      "When threshold = 0.16 - Sensitivity: 0.9792, Specificity: 0.8910\n",
      "When threshold = 0.17 - Sensitivity: 0.9792, Specificity: 0.8910\n",
      "When threshold = 0.18 - Sensitivity: 0.9792, Specificity: 0.8974\n",
      "When threshold = 0.19 - Sensitivity: 0.9792, Specificity: 0.8974\n",
      "When threshold = 0.20 - Sensitivity: 0.9792, Specificity: 0.9038\n",
      "When threshold = 0.21 - Sensitivity: 0.9792, Specificity: 0.9038\n",
      "When threshold = 0.22 - Sensitivity: 0.9792, Specificity: 0.9167\n",
      "When threshold = 0.23 - Sensitivity: 0.9792, Specificity: 0.9295\n",
      "When threshold = 0.24 - Sensitivity: 0.9792, Specificity: 0.9295\n",
      "When threshold = 0.25 - Sensitivity: 0.9792, Specificity: 0.9295\n",
      "When threshold = 0.26 - Sensitivity: 0.9792, Specificity: 0.9295\n",
      "When threshold = 0.27 - Sensitivity: 0.9722, Specificity: 0.9295\n",
      "When threshold = 0.28 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.29 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.30 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.31 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.32 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.33 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.34 - Sensitivity: 0.9722, Specificity: 0.9359\n",
      "When threshold = 0.35 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.36 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.37 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.38 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.39 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.40 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.41 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.42 - Sensitivity: 0.9653, Specificity: 0.9423\n",
      "When threshold = 0.43 - Sensitivity: 0.9653, Specificity: 0.9487\n",
      "When threshold = 0.44 - Sensitivity: 0.9653, Specificity: 0.9487\n",
      "When threshold = 0.45 - Sensitivity: 0.9583, Specificity: 0.9551\n",
      "When threshold = 0.46 - Sensitivity: 0.9514, Specificity: 0.9551\n",
      "When threshold = 0.47 - Sensitivity: 0.9514, Specificity: 0.9679\n",
      "When threshold = 0.48 - Sensitivity: 0.9514, Specificity: 0.9679\n",
      "When threshold = 0.49 - Sensitivity: 0.9514, Specificity: 0.9679\n",
      "When threshold = 0.51 - Sensitivity: 0.9514, Specificity: 0.9679\n",
      "When threshold = 0.52 - Sensitivity: 0.9514, Specificity: 0.9679\n",
      "When threshold = 0.53 - Sensitivity: 0.9444, Specificity: 0.9679\n",
      "When threshold = 0.54 - Sensitivity: 0.9444, Specificity: 0.9679\n",
      "When threshold = 0.55 - Sensitivity: 0.9444, Specificity: 0.9679\n",
      "When threshold = 0.56 - Sensitivity: 0.9444, Specificity: 0.9679\n",
      "When threshold = 0.57 - Sensitivity: 0.9375, Specificity: 0.9679\n",
      "When threshold = 0.58 - Sensitivity: 0.9375, Specificity: 0.9679\n",
      "When threshold = 0.59 - Sensitivity: 0.9375, Specificity: 0.9744\n",
      "When threshold = 0.60 - Sensitivity: 0.9306, Specificity: 0.9744\n",
      "When threshold = 0.61 - Sensitivity: 0.9167, Specificity: 0.9744\n",
      "When threshold = 0.62 - Sensitivity: 0.9097, Specificity: 0.9744\n",
      "When threshold = 0.63 - Sensitivity: 0.9097, Specificity: 0.9744\n",
      "When threshold = 0.64 - Sensitivity: 0.9097, Specificity: 0.9744\n",
      "When threshold = 0.65 - Sensitivity: 0.9097, Specificity: 0.9744\n",
      "When threshold = 0.66 - Sensitivity: 0.9097, Specificity: 0.9744\n",
      "When threshold = 0.67 - Sensitivity: 0.8889, Specificity: 0.9744\n",
      "When threshold = 0.68 - Sensitivity: 0.8889, Specificity: 0.9744\n",
      "When threshold = 0.69 - Sensitivity: 0.8750, Specificity: 0.9744\n",
      "When threshold = 0.70 - Sensitivity: 0.8681, Specificity: 0.9744\n",
      "When threshold = 0.71 - Sensitivity: 0.8611, Specificity: 0.9744\n",
      "When threshold = 0.72 - Sensitivity: 0.8611, Specificity: 0.9808\n",
      "When threshold = 0.73 - Sensitivity: 0.8542, Specificity: 0.9872\n",
      "When threshold = 0.74 - Sensitivity: 0.8472, Specificity: 0.9872\n",
      "When threshold = 0.75 - Sensitivity: 0.8472, Specificity: 0.9872\n",
      "When threshold = 0.76 - Sensitivity: 0.8472, Specificity: 0.9872\n",
      "When threshold = 0.77 - Sensitivity: 0.8472, Specificity: 0.9872\n",
      "When threshold = 0.78 - Sensitivity: 0.8472, Specificity: 0.9872\n",
      "When threshold = 0.79 - Sensitivity: 0.8472, Specificity: 0.9872\n",
      "When threshold = 0.80 - Sensitivity: 0.8333, Specificity: 0.9872\n",
      "When threshold = 0.81 - Sensitivity: 0.8264, Specificity: 0.9872\n",
      "When threshold = 0.82 - Sensitivity: 0.8264, Specificity: 0.9872\n",
      "When threshold = 0.83 - Sensitivity: 0.8264, Specificity: 0.9936\n",
      "When threshold = 0.84 - Sensitivity: 0.8194, Specificity: 1.0000\n",
      "When threshold = 0.85 - Sensitivity: 0.8125, Specificity: 1.0000\n",
      "When threshold = 0.86 - Sensitivity: 0.8056, Specificity: 1.0000\n",
      "When threshold = 0.87 - Sensitivity: 0.7986, Specificity: 1.0000\n",
      "When threshold = 0.88 - Sensitivity: 0.7986, Specificity: 1.0000\n",
      "When threshold = 0.89 - Sensitivity: 0.7847, Specificity: 1.0000\n",
      "When threshold = 0.90 - Sensitivity: 0.7778, Specificity: 1.0000\n",
      "When threshold = 0.91 - Sensitivity: 0.7639, Specificity: 1.0000\n",
      "When threshold = 0.92 - Sensitivity: 0.7500, Specificity: 1.0000\n",
      "When threshold = 0.93 - Sensitivity: 0.7500, Specificity: 1.0000\n",
      "When threshold = 0.94 - Sensitivity: 0.7431, Specificity: 1.0000\n",
      "When threshold = 0.95 - Sensitivity: 0.7292, Specificity: 1.0000\n",
      "When threshold = 0.96 - Sensitivity: 0.6944, Specificity: 1.0000\n",
      "When threshold = 0.97 - Sensitivity: 0.6319, Specificity: 1.0000\n",
      "When threshold = 0.98 - Sensitivity: 0.5556, Specificity: 1.0000\n",
      "When threshold = 0.99 - Sensitivity: 0.3333, Specificity: 1.0000\n",
      "When threshold = 1.00 - Sensitivity: 0.0000, Specificity: 1.0000\n"
     ]
    }
   ],
   "source": [
    "def confusion_matrix(y_pred, y):\n",
    "    '''\n",
    "    y_pred - predict classes\n",
    "    y - actual classes\n",
    "    '''\n",
    "    # Get the number of classes\n",
    "    n_classes = len(np.unique(y))\n",
    "    \n",
    "    # Initialize the confusion matrix with zeros\n",
    "    cm = np.zeros((n_classes, n_classes))\n",
    "    \n",
    "    # Fill the confusion matrix\n",
    "    for pred, actual in zip(y_pred, y):\n",
    "        cm[pred][actual] += 1\n",
    "    \n",
    "    return cm\n",
    "\n",
    "def get_prediction_from_proba(y_proba, threshold=0.5):\n",
    "    '''\n",
    "    y_proba - predict probabilities\n",
    "    threshold - threshold of probability\n",
    "    \n",
    "    y_pred - predict classes, y_proba > threshold is positive, otherwise negative\n",
    "    '''\n",
    "    y_pred = np.where(y_proba > threshold, 1, 0)\n",
    "    return y_pred\n",
    "\n",
    "def get_sensitivity_specificity(y_proba, y, threshold=0.5):\n",
    "    '''\n",
    "    Compute sensitivity and specificity of binary classification\n",
    "    y_pred - predict classes\n",
    "    y - actual classes\n",
    "    threshold - threshold of probability\n",
    "    '''\n",
    "    epsilon = 1e-10\n",
    "    \n",
    "    # Get the confusion matrix\n",
    "    y_pred = get_prediction_from_proba(y_proba, threshold)\n",
    "    cm = confusion_matrix(y_pred, y)\n",
    "    \n",
    "    # Compute sensitivity and specificity\n",
    "    sensitivity = cm[1, 1] / (cm[1, 1] + cm[0, 1] + epsilon)\n",
    "    specificity = cm[0, 0] / (cm[0, 0] + cm[1, 0] + epsilon)\n",
    "    \n",
    "    return sensitivity, specificity\n",
    "\n",
    "# Get predictions from probabilities\n",
    "sen_spec_svm = []\n",
    "for threshold in np.linspace(0, 1, 100):\n",
    "    sensitivity, specificity = get_sensitivity_specificity(y_proba_svm, y_test, threshold)\n",
    "    print(f\"When threshold = {threshold:0.2f} - Sensitivity: {sensitivity:.4f}, Specificity: {specificity:.4f}\")\n",
    "    sen_spec_svm.append([sensitivity, specificity])\n",
    "\n",
    "# Convert to numpy array\n",
    "sen_spec_svm = np.array(sen_spec_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ed547d-e0a7-471a-9d45-fa5074235ae4",
   "metadata": {},
   "source": [
    "### 绘制Specificity-Sensitivity曲线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "73b05d33-ef30-49dc-8f5a-dbf8f04c7306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use sen_spec_svm to draw specificity-sensitivity curve\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(sen_spec_svm[:, 1], sen_spec_svm[:, 0], marker='.', color='tab:blue', label='SVM Model')\n",
    "ax.set_xlabel('Specificity', fontsize=label_size)\n",
    "ax.set_ylabel('Sensitivity', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Set axis limits\n",
    "ax.set_xlim([0, 1.01])\n",
    "ax.set_ylim([0, 1.01])\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('Specificity_Sensitivity_Curve.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ab5528-0874-4c6b-895a-5d320b5d67f2",
   "metadata": {},
   "source": [
    "### 绘制ROC曲线并计算AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c42378d4-5af1-43a8-abc0-237366258e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under the ROC Curve (AUC): 0.9924\n"
     ]
    }
   ],
   "source": [
    "# Use sen_spec_svm to draw ROC curve\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(1 - sen_spec_svm[:, 1], sen_spec_svm[:, 0], marker='.', color='tab:red', label='SVM Model')\n",
    "ax.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random Classifier')\n",
    "ax.set_xlabel('False Positive Rate (1 - Specificity)', fontsize=label_size)\n",
    "ax.set_ylabel('True Positive Rate (Sensitivity)', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Set axis limits\n",
    "ax.set_xlim([-0.01, 1.00])\n",
    "ax.set_ylim([-0.01, 1.01])\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('ROC_Curve.png', dpi=300)\n",
    "\n",
    "# Add surface\n",
    "ax.fill_between(1 - sen_spec_svm[:, 1], sen_spec_svm[:, 0], color='tab:blue', alpha=0.2)\n",
    "\n",
    "plt.savefig('AUC.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Calculate AUC\n",
    "from sklearn.metrics import auc\n",
    "roc_auc = auc(1 - sen_spec_svm[:, 1], sen_spec_svm[:, 0])\n",
    "print(f\"Area Under the ROC Curve (AUC): {roc_auc:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a7c9c04-ce88-4cae-835d-efd5f43de2af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
